
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "14"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 102 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 98 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	93.159507	71.555823	160.552033	139.527949	31.951454	31.517824	88.462442	87.161768	0.0190	0.0182	0.0010	1.131112	1.137275
2459593	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	51.794619	96.783559	153.375484	179.681219	55.218441	69.551862	127.051142	212.752223	0.0196	0.0181	0.0018	1.112991	1.104249
2459592	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	7.050176	15.211903	71.476620	71.187833	98.920932	77.166199	131.163714	86.706301	0.0203	0.0177	0.0023	nan	nan
2459591	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	91.354454	95.333975	192.676371	203.426543	41.428474	69.137590	184.511612	332.748104	0.0200	0.0175	0.0017	1.130444	1.127884
2459590	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	180.189369	128.114993	189.513973	178.680286	54.276354	72.304912	283.586420	251.439484	0.0183	0.0185	0.0005	1.119683	1.123603
2459589	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	339.590075	338.910602	inf	inf	50.646717	53.963088	374.099921	392.775205	nan	nan	nan	0.000000	0.000000
2459588	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	184.947163	68.731276	272.330437	201.860407	39.166854	36.018667	58.402499	67.238134	0.0180	0.0177	0.0006	1.102158	1.110228
2459587	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	309.026610	108.640695	428.490795	287.881202	116.472788	54.620135	744.984436	398.265432	nan	nan	nan	1.157899	1.170531
2459586	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	130.007072	105.155244	62.599512	61.722888	18.368678	18.538533	347.642736	397.914934	0.0185	0.0177	0.0014	1.131090	1.131888
2459585	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	65.857432	66.322395	46.864436	47.114438	12.960716	14.373833	696.882857	690.353595	0.0197	0.0175	0.0011	0.000000	0.000000
2459584	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	142.825524	146.858218	252.767025	275.889810	21.257713	35.959112	116.352747	153.879327	0.0193	0.0170	0.0013	1.240562	1.230855
2459583	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	170.058174	76.814102	91.694759	78.265167	35.733682	54.725758	262.566544	289.160032	0.0186	0.0175	0.0008	1.190993	1.184771
2459582	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	133.535061	90.356085	87.935345	72.982661	80.013501	60.337313	367.613530	259.376566	0.0179	0.0172	0.0006	1.126197	1.131305
2459581	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	205.739316	110.168438	94.190565	82.203882	40.029519	42.229036	338.485630	407.559178	0.0191	0.0172	0.0015	1.123387	1.124647
2459580	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	119.061761	76.983812	90.077770	78.462780	33.353492	50.708562	247.282775	253.539909	0.0185	0.0174	0.0006	1.121894	1.124374
2459579	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	148.750570	82.378979	102.654315	90.396263	61.897870	78.156491	515.967151	451.499855	0.0186	0.0173	0.0008	1.125874	1.126216
2459578	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	16.681707	9.525942	54.461560	31.321794	434.689890	180.797188	2017.438437	1040.357507	0.0188	0.0176	0.0004	nan	nan
2459577	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	162.403544	119.389500	88.150864	76.746538	87.591003	44.600107	420.484102	350.044632	0.0181	0.0172	0.0004	1.106860	1.110743
2459576	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	167.038340	80.902709	69.556339	57.649994	64.587457	39.425920	490.652045	383.335888	0.0186	0.0172	0.0008	1.106704	1.107322
2459575	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	125.075187	66.860690	72.042363	56.607575	34.423929	47.169070	56.000377	37.688984	0.0191	0.0178	0.0005	0.000000	0.000000
2459574	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	75.572604	55.764262	55.589875	49.936894	54.467391	37.179890	216.558598	152.153611	0.0187	0.0179	0.0004	1.084873	1.091111
2459573	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	73.574685	102.020256	91.104260	110.045687	44.266706	85.430489	262.651781	531.963236	0.0193	0.0171	0.0015	0.000000	0.000000
2459572	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	78.625107	83.709044	55.534409	55.516487	29.466695	34.296939	595.835059	627.819247	0.0184	0.0175	0.0006	1.066568	1.068808
2459571	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	9.502643	11.395731	42.449542	41.456273	221.128272	212.189014	1531.026508	1257.287337	0.0186	0.0176	0.0009	nan	nan
2459570	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	157.219322	130.529988	89.970363	94.682705	38.638809	46.166970	52.130866	88.111775	0.0181	0.0166	0.0016	1.100607	1.095086
2459566	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	115.219785	82.920200	78.665766	75.206054	48.862281	63.556344	247.906373	199.082555	0.0191	0.0172	0.0015	0.000000	0.000000
2459565	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	-1.318369	-1.318369	-1.270881	-1.270881	-0.651706	-0.651706	-0.774639	-0.774639	nan	nan	nan	0.000000	0.000000
2459564	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	154.671049	64.081502	91.928730	70.860960	12.507200	9.011439	297.726015	215.871423	0.0182	0.0176	0.0005	1.110349	1.116046
2459563	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	155.436690	80.562358	172.944736	157.062426	42.872935	53.453716	463.654504	483.319821	0.0187	0.0172	0.0011	0.000000	0.000000
2459562	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	73.957299	50.664929	39.112236	32.802519	11.445226	6.933566	187.299461	139.277388	0.0175	0.0182	0.0014	1.113111	1.127295
2459561	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	159.486019	52.852304	51.510909	35.494858	18.325625	14.863853	548.484357	337.005389	0.0178	0.0175	0.0005	1.093578	1.105893
2459560	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	109.755813	47.786691	43.121714	33.705191	10.386938	9.093026	297.115155	207.206520	0.0187	0.0177	0.0005	1.099842	1.111924
2459559	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	73.468435	69.146242	40.632108	38.854580	10.894215	9.233880	316.283853	241.448775	0.0184	0.0171	0.0015	1.113087	1.116094
2459558	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	69.385548	131.847253	72.437150	96.405183	41.422050	92.612706	299.025296	670.147353	0.0195	0.0167	0.0020	1.136303	1.128087
2459557	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0183	0.0181	-0.0001	nan	nan
2459556	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	201.611399	95.329409	115.643061	83.296850	56.432304	43.333199	1453.468445	757.923028	0.0183	0.0173	0.0006	1.125888	1.131616
2459554	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0180	0.0169	0.0006	nan	nan
2459553	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0179	0.0175	0.0005	nan	nan
2459552	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	94.364605	78.561866	109.211740	96.567114	49.829752	50.799960	243.113793	280.703320	0.0186	0.0178	0.0005	0.000000	0.000000
2459551	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	53.865366	73.057773	43.699748	51.198263	12.341702	23.873399	302.010421	569.119341	0.0184	0.0167	0.0012	1.119564	1.117045
2459550	RF_maintenance	-	96.71%	96.71%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0464	0.0450	0.0012	nan	nan
2459549	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	190.931847	65.116227	119.799300	75.414692	80.439371	42.652007	797.613247	316.725054	0.0185	0.0179	0.0010	1.120993	1.132282
2459542	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	161.693358	81.288648	197.822354	151.775019	7.490351	3.707969	532.999902	240.860852	0.0177	0.0177	0.0006	1.119129	1.125406
2459541	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.129893	10.681245	42.914099	43.584187	647.681571	643.860605	49.582448	34.504346	0.0190	0.0174	0.0013	1.144274	1.140570
2459540	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	121.273658	88.043890	45.051297	44.035757	25.243406	46.502582	210.854386	286.710602	0.0184	0.0171	0.0010	1.151393	1.141850
2459536	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	153.330011	123.315502	147.397664	149.780339	59.860081	103.454544	794.865256	1071.031022	0.0182	0.0169	0.0010	1.129219	1.131248
2459535	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	66.723367	74.148830	36.149711	38.715201	12.009059	39.652135	161.473117	310.592992	0.0181	0.0170	0.0013	1.121540	1.120883
2459534	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	92.671001	50.339546	36.117863	31.115973	15.699841	14.379046	163.658206	148.661700	0.0183	0.0177	0.0006	1.123293	1.126669
2459533	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	102.952110	72.495879	83.109918	70.392935	43.511012	41.190960	540.183050	383.752488	0.0183	0.0180	0.0007	1.135148	1.138552
2459532	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	144.228951	90.587265	112.271159	109.537963	56.746435	58.014473	455.914702	442.824194	0.0189	0.0170	0.0007	1.135009	1.137586
2459530	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	78.067620	71.368386	34.701885	36.251714	15.332121	25.509348	221.703835	335.931477	0.0184	0.0172	0.0007	1.123502	1.125067
2459527	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	120.903500	78.206592	124.899512	108.152829	131.408218	103.990400	311.202282	263.542789	0.0185	0.0170	0.0005	0.000000	0.000000
2459523	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	165.659976	160.171229	124.437983	142.545391	68.951824	129.029974	512.396678	1024.129688	nan	nan	nan	1.361750	1.360840
2459522	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	88.804174	88.026834	105.671177	110.567289	90.601385	78.079207	434.988510	381.532319	0.0188	0.0174	0.0014	1.178336	1.165125
2459513	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	11.103564	13.349034	39.706959	33.225884	250.109796	237.141570	1233.574088	993.335725	0.0204	0.0185	0.0014	nan	nan
2459508	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	73.185952	78.602619	101.037644	96.553763	42.706682	39.437139	73.951099	71.325888	0.0188	0.0182	0.0008	0.000000	0.000000
2459505	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	78.771754	115.055011	94.349393	113.926518	59.986274	76.760741	367.462531	563.980919	0.0118	0.0092	0.0049	1.137311	1.118043
2459503	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	86.646867	101.192735	166.560961	163.891705	56.543431	61.253168	548.648302	403.381309	0.0092	0.0082	0.0038	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 14 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 14, Node 0:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 14: 2459626

Antenna 14: 2459625

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶